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[BugFix] Fixed shape for MultiStep returns + Distributional loss - #2270
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Thanks for this contribution! I left a couple of comments, would you have time to address them?
| pns_a = pns_a.to("cpu") | ||
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| Tz = reward + (1 - terminated.to(reward.dtype)) * discount * support | ||
| Tz = reward + (1 - terminated.to(reward.dtype)) * discount.unsqueeze(-1) * support.repeat(batch_size, 1) |
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Looks good
I think we're making a couple of assumptions about discount and support here.
I would feel safer if we had a block before this where we check explicitly the shapes:
if not check(discount.shape):
raise RuntimeError(f"Expected `discount` to have either shape X or Y, got {discount.shape}")
if not other_check(support.shape):
raise RuntimeError(f"Expected `support` to have either shape X or Y, got {support.shape}")(check and other_check are just placeholders)
If possible I would also make an instance of this loss class with the support and discount shape that you are using to test that it works ok (see test_cost.py:TestDQN:test_distributional_dqn)
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We should also make sure that discount is a tensor:
discount = torch.as_tensor(discount)should do it (it's a no-op if it's already a tensor)
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Description
I encountered an error when combining MultiStep returns + Distributional DQN Loss. Because this line assumes discount is an integer but if using MultiStep returns it is a tensor of size
(batch_size,).This PR only changes that line to:
Tz = reward + (1 - terminated.to(reward.dtype)) * discount.unsqueeze(-1) * support.repeat(batch_size, 1)which gives the same results if the discount is an integer but fixes the shape error if it is a batch.
Solves issue #2269
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